pageindex-open 0.1.1

Truly open PageIndex implementation

pageindex-open

Truly open pageindex RAG package

This package was inspired by PageIndex. I took inspiration from the concepts outlined and came up with my own implementation. I was not satisfied with the package as examples focus on the SaaS part of things.

This package works by simply converting your PDFs into a tree then the most relevent section is decided and used. This contrasts with chuncking where similarity is compared using embeddings.

Why?

• 🧠 Reasoning-backed: AI routes and answers using structured context, not just similarity.
• ⚡ Contrast to RAG: Traditional RAG retrieves random chunks by embedding similarity: here, relevance is hierarchical and precise.
• 🌳 Tree-structured: Sections, subsections, and headings preserved: your document is understood, not just searched.
• 🔢 Top-K retrieval: Combine multiple relevant sections for richer answers, avoiding “partial context” problems.
• ✂️ Text-on-demand: Only the node text is used, no bloated storage or duplication.
• 💾 Persistent cache: Markdown + tree saved separately: queries can be re-run without touching the PDF.
• 📄 Markdown source: Human-readable, diffable, and editable: not a black-box blob of vectors.
• 🔄 Reusable & update-friendly: Swap LLMs, add PDFs, or refresh sections without breaking the index.
• 📦 Clean Python API: build_index(), query(), load_index(): intuitive for devs.
• 💪 Production-ready design: Modular, maintainable, and scalable for large document QA workflows.

Quickstart

For one document, the example is as follows:

# export GEMINI_API_KEY=AI...
# uses litellm under the hood
from pageindex_open import *

PDF_FILE = "/path/to/file/2023-annual-report-truncated.pdf"
QUERY = "what about financial stability?"


pio = PIO(PDF_FILE)
pio.build_index() 

answer = pio.query(QUERY, top_k=2)
print(answer)

Application

This works for structured documents and you applies to sectors like finance and legal

API

Specify more

pio = PIO(PDF_FILE, model_name="modelprovider/model-name", llm_client=litellm_client_if_any)

Load index

from pageindex_open import *

PDF_FILE = "/path/to/file/2023-annual-report-truncated.pdf"
QUERY = "what about financial stability?"


pio = PIO(PDF_FILE)
pio.load_index("/path/mdfile.md", "/path/file.tree.json") # files that were created using build_index

Roadmap

• Multi-document
• Document processing backend
• Save config options
• Add chat with docs feature